Project

treat

0.53
No commit activity in last 3 years
No release in over 3 years
There's a lot of open issues
treatlouismullie/treatHomepageDocumentationSource CodeBug TrackerWiki
Treat is a natural language processing framework for Ruby.
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 Popularity
Downloads
91,898
Stars
1,368
Forks
125
Watchers
68
 Releases
Current version
2.1.0
Total releases
28
First release
2012-01-24
Latest release
2014-07-17
 Issues
Open Issues
35
Closed Issues
57
Total Issues
92
Issue Closure Rate
61%
 Pull Requests
Open Pull Requests
0
Closed Pull Requests
5
Merged Pull Requests
29
Pull Request Acceptance Rate
85%
 Development
Primary Language
Ruby
Average date of last 50 commits
2014-09-12
Reverse Dependencies
2

 Dependencies

Development

rake
>= 0
rspec
>= 0
simplecov
>= 0

Runtime

birch
>= 0
schiphol
>= 0
yomu
>= 0
 Project Readme

Build Status Code Climate

Treat Logo

New in v2.0.5: OpenNLP integration and Yomu support

Treat is a toolkit for natural language processing and computational linguistics in Ruby. The Treat project aims to build a language- and algorithm- agnostic NLP framework for Ruby with support for tasks such as document retrieval, text chunking, segmentation and tokenization, natural language parsing, part-of-speech tagging, keyword extraction and named entity recognition. Learn more by taking a quick tour or by reading the manual.

Features

  • Text extractors for PDF, HTML, XML, Word, AbiWord, OpenOffice and image formats (Ocropus).
  • Text chunkers, sentence segmenters, tokenizers, and parsers (Stanford & Enju).
  • Lexical resources (WordNet interface, several POS taggers for English).
  • Language, date/time, topic words (LDA) and keyword (TF*IDF) extraction.
  • Word inflectors, including stemmers, conjugators, declensors, and number inflection.
  • Serialization of annotated entities to YAML, XML or to MongoDB.
  • Visualization in ASCII tree, directed graph (DOT) and tag-bracketed (standoff) formats.
  • Linguistic resources, including language detection and tag alignments for several treebanks.
  • Machine learning (decision tree, multilayer perceptron, LIBLINEAR, LIBSVM).
  • Text retrieval with indexation and full-text search (Ferret).

Contributing

I am actively seeking developers that can help maintain and expand this project. You can find a list of ideas for contributing to the project here.

Authors

Lead developper: @louismullie [Twitter]

Contributors:

  • @bdigital
  • @automatedtendencies
  • @LeFnord
  • @darkphantum
  • @whistlerbrk
  • @smileart
  • @erol

License

This software is released under the GPL License and includes software released under the GPL, Ruby, Apache 2.0 and MIT licenses.